WO2017094121A1 - Voice recognition device, voice emphasis device, voice recognition method, voice emphasis method, and navigation system - Google Patents
Voice recognition device, voice emphasis device, voice recognition method, voice emphasis method, and navigation system Download PDFInfo
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Definitions
- the present invention relates to a speech recognition technology and a speech enhancement technology, and particularly to a technology corresponding to use under various noise environments.
- noise suppression processing When performing speech recognition using speech with superimposed noise, it is common to perform processing for suppressing superimposed noise (hereinafter referred to as noise suppression processing) before performing speech recognition processing.
- noise suppression processing processing for suppressing superimposed noise
- the noise suppression process is a spectrum removal process that is strong against stationary noise, the removal process against unsteady noise becomes weak.
- the noise suppression process is a process that has high followability to non-stationary noise, the process is low in followability to steady noise.
- integration of speech recognition results or selection of speech recognition results has been used as a method for solving such problems.
- noise is generated by two noise suppression units that perform, for example, suppression processing with high followability to stationary noise and suppression processing with high followability to non-stationary noise.
- the two voices are acquired by suppressing the voice, and the voices are recognized by the two voice recognition units with respect to the obtained two voices.
- the two speech recognition results obtained by speech recognition are integrated using a speech combining method such as ROVER (Recognition Output Voting Error Reduction), or the speech recognition result with the highest likelihood is selected from the two speech recognition results, The integrated or selected speech recognition result is output.
- ROVER Recognition Output Voting Error Reduction
- Patent Document 1 discloses a speech recognition device that calculates the likelihood of each acoustic feature parameter of input noise for each probability speech model and selects a sound probability acoustic model from the likelihood.
- Japanese Patent Laid-Open No. 2004-228688 discloses a method in which noise is removed from an input target signal, preprocessing for extracting feature amount data representing the characteristics of the target signal is performed, and then the target signal is determined according to the shape of the clustering map of the competitive neural network. Is classified into a plurality of categories, and a signal identification device that automatically selects processing contents is disclosed.
- Patent Document 1 uses the likelihood of each acoustic feature parameter of the input noise for each probability speech model, a noise suppression process that can obtain a good speech recognition rate or acoustic index is selected. There was a problem that it might not be.
- the technique disclosed in Patent Document 2 although the target signal is clustered, clustering associated with the speech recognition rate or the acoustic index is not performed. There was a problem that the obtained noise suppression processing might not be selected.
- the above two methods share the need for noise-suppressed speech for performance prediction, so all candidate noise suppression processing must be performed once during both learning and use. There was a problem.
- the present invention has been made in order to solve the above-described problems, and provides a good speech recognition rate or acoustic index only from noise speech data without performing noise suppression processing at the time of use in order to select a noise suppression method. It is an object to select a noise suppression process that provides high accuracy.
- a speech recognition apparatus includes a plurality of noise suppression units that perform noise suppression processing using different methods on input noise speech data, and speech recognition of speech data in which noise signals are suppressed by the noise suppression unit.
- a speech recognition unit that performs a noise recognition process, and a prediction unit that predicts a speech recognition rate obtained when noise suppression processing is performed on each of the noise speech data by a plurality of noise suppression units, based on the acoustic feature amount of the input noise speech data, And a suppression method selection unit that selects a noise suppression unit that performs noise suppression processing on noise speech data from a plurality of noise suppression units based on the speech recognition rate predicted by the prediction unit.
- the present invention it is possible to select a noise suppression process that provides a good speech recognition rate or acoustic index without performing a noise suppression process to select a noise suppression method.
- FIG. 1 is a block diagram showing a configuration of a speech recognition apparatus according to Embodiment 1.
- FIG. 2A and 2B are diagrams showing a hardware configuration of the speech recognition apparatus according to the first embodiment.
- 3 is a flowchart showing an operation of the speech recognition apparatus according to the first embodiment.
- 4 is a block diagram illustrating a configuration of a speech recognition apparatus according to Embodiment 2.
- FIG. 6 is a flowchart showing the operation of the speech recognition apparatus according to the second embodiment.
- FIG. 6 is a block diagram illustrating a configuration of a speech recognition apparatus according to a third embodiment. It is a figure which shows the structural example of the recognition rate database of the speech recognition apparatus which concerns on Embodiment 3.
- FIG. 10 is a flowchart showing the operation of the speech recognition apparatus according to the third embodiment.
- FIG. 10 is a block diagram illustrating a configuration of a speech enhancement device according to a fourth embodiment.
- 10 is a flowchart illustrating an operation of the speech enhancement device according to the fourth embodiment.
- FIG. 10 is a functional block diagram showing a configuration of a navigation system according to a fifth embodiment.
- FIG. 1 is a block diagram showing the configuration of the speech recognition apparatus 100 according to the first embodiment.
- the speech recognition apparatus 100 includes a first prediction unit 1, a suppression method selection unit 2, a noise suppression unit 3, and a speech recognition unit 4.
- the first prediction unit 1 is composed of a regressor.
- a neural network hereinafter referred to as NN
- NN a neural network
- an acoustic feature that is generally used such as Mel-frequency Cepstral Coefficient (MFCC) or filter bank feature, is used to directly calculate a speech recognition rate of 0 to 1 as a regressor.
- the error back-propagation method is a learning method in which, when given learning data is given, the coupling load / bias between the layers is corrected so that the error between the learning data and the output of the NN becomes small.
- the first predicting unit 1 predicts the speech recognition rate of the input acoustic feature amount by, for example, the NN having the input as the acoustic feature amount and the output as the speech recognition rate.
- the suppression method selection unit 2 refers to the speech recognition rate predicted by the first prediction unit 1, and selects the noise suppression unit 3 that performs noise suppression from the plurality of noise suppression units 3a, 3b, and 3c.
- the suppression method selection unit 2 outputs a control instruction to perform noise suppression processing on the selected noise suppression unit 3.
- the noise suppression unit 3 includes a plurality of noise suppression units 3a, 3b, and 3c, and each noise suppression unit 3a, 3b, and 3c performs different noise suppression processing on the input noise voice data.
- different noise suppression processing for example, an adaptive filter method using a spectrum removal method (SS), a learning identification method (Normalized Least Mean Square Algorithm; NLMS algorithm), or a method using NN such as Denoising auto encoder can be applied.
- SS spectrum removal method
- NLMS algorithm Normalized Least Mean Square Algorithm
- NN Denoising auto encoder
- noise suppression units 3a, 3b, and 3c performs the noise suppression processing is determined based on a control instruction input from the suppression method selection unit 2.
- FIG. 1 an example in which the three noise suppression units 3a, 3b, and 3c are configured is shown, but the number of components is not limited to three and can be changed as appropriate.
- the voice recognition unit 4 performs voice recognition on the voice data in which the noise signal is suppressed by the noise suppression unit 3, and outputs a voice recognition result.
- speech recognition speech recognition processing is performed using, for example, an acoustic model based on Gaussian mix model or Deep neural network and a language model based on n-gram. Note that since the voice recognition process can be configured by applying a known technique, a detailed description thereof will be omitted.
- the first prediction unit 1, the suppression method selection unit 2, the noise suppression unit 3, and the speech recognition unit 4 of the speech recognition apparatus 100 are realized by a processing circuit.
- the processing circuit may be dedicated hardware or may be a CPU (Central Processing Unit) that executes a program stored in a memory, a processing device, a processor, and the like.
- FIG. 2A shows a hardware configuration of the speech recognition apparatus 100 according to Embodiment 1, and shows a block diagram when the processing circuit is executed by hardware.
- each function of the first prediction unit 1, the suppression method selection unit 2, the noise suppression unit 3, and the speech recognition unit 4 is realized by the processing circuit.
- the functions of the respective units may be integrated and realized by a processing circuit.
- FIG. 2B shows a hardware configuration of the speech recognition apparatus 100 according to Embodiment 1, and shows a block diagram when the processing circuit is executed by software.
- each function of the first prediction unit 1, the suppression method selection unit 2, the noise suppression unit 3, and the speech recognition unit 4 is software, firmware, or software. Realized by combination with firmware.
- Software and firmware are described as programs and stored in the memory 103.
- the processor 102 reads out and executes the program stored in the memory 103, thereby executing the function of each unit.
- the memory 103 corresponds to, for example, a nonvolatile or volatile semiconductor memory such as a RAM, a ROM, and a flash memory, a magnetic disk, and an optical disk.
- the processing circuit can realize the above-described functions by hardware, software, firmware, or a combination thereof.
- the 1st prediction part 1 to which the regressor is applied is comprised by NN which uses an acoustic feature-value as an input and uses an output as a speech recognition rate.
- the first prediction unit 1 predicts the speech recognition rate by each of the noise suppression units 3a, 3b, and 3c by NN. That is, the first predicting unit 1 calculates a speech recognition rate for each frame of the acoustic feature amount when different noise suppression processes are applied.
- the suppression method selection unit 2 refers to the speech recognition rate when the noise suppression units 3a, 3b, and 3c calculated by the first prediction unit 1 are applied, and noise suppression that leads to a speech recognition result with the highest speech recognition rate The unit 3 is selected, and a control instruction is output to the selected noise suppression unit 3.
- FIG. 3 is a flowchart showing the operation of the speech recognition apparatus 100 according to the first embodiment. It is assumed that the voice recognition apparatus 100 receives noise voice data and an acoustic feature amount of the noise voice data via an external microphone or the like. It is assumed that the acoustic feature amount of the noise voice data is calculated by an external feature amount calculation unit.
- the first predicting unit 1 performs each noise by NN in a frame unit of short-time Fourier transform of the input acoustic feature quantity.
- a speech recognition rate is predicted when noise suppression processing is performed by the suppression units 3a, 3b, and 3c (step ST2).
- step ST2 is repeatedly performed for a plurality of set frames.
- the first prediction unit 1 obtains the average, maximum value, or minimum value of the speech recognition rate predicted for each frame and a plurality of frames in step ST2, and performs processing in each noise suppression unit 3a, 3b, 3c
- Each prediction recognition rate is calculated (step ST3).
- the first prediction unit 1 associates the calculated prediction recognition rate with each noise suppression unit 3a, 3b, 3c and outputs it to the suppression method selection unit 2 (step ST4).
- the suppression technique selection unit 2 refers to the prediction recognition rate output in step ST4, selects the noise suppression unit 3 that exhibits the highest prediction recognition rate, and performs noise suppression processing on the selected noise suppression unit 3.
- a control instruction is output to (step ST5).
- the noise suppression unit 3 to which the control instruction is input in step ST5 performs a process of suppressing the noise signal with respect to the actual noise voice data input in step ST1 (step ST6).
- the voice recognition unit 4 performs voice recognition on the voice data in which the noise signal is suppressed in step ST6, acquires a voice recognition result, and outputs it (step ST7). Thereafter, the flowchart returns to the process of step ST1 and repeats the process described above.
- the first predicting unit 1 configured by the regressor, configured by the NN having the acoustic feature quantity as input and the output as the speech recognition rate
- the noise suppression unit 3 that leads the speech recognition result with the highest speech recognition rate is selected from the plurality of noise suppression units 3, and a control instruction is given to the selected noise suppression unit 3
- a noise suppression unit 3 that performs noise suppression processing of noise speech data based on a control instruction of the suppression method selection unit 2
- a suppression method selection unit 2 that outputs a plurality of processing units to which a plurality of noise suppression methods are applied.
- the voice recognition unit 4 that performs voice recognition of the voice data subjected to the noise suppression processing is provided, so that the noise suppression can be performed without increasing the processing amount of voice recognition and selecting a noise suppression method.
- Effective noise suppression without processing It can be selected.
- the noise suppression processing is performed by all three methods and the best noise suppression processing is selected based on the result.
- a method that will have the best performance can be predicted in advance. Therefore, noise suppression processing is performed by performing noise suppression processing only with the selected method. There is an advantage that the amount of calculation for processing can be reduced.
- FIG. 4 is a block diagram illustrating a configuration of the speech recognition apparatus 100a according to the second embodiment.
- the speech recognition apparatus 100a according to Embodiment 2 replaces the first prediction unit 1 and the suppression method selection unit 2 of the speech recognition apparatus 100 described in Embodiment 1, with a second prediction unit 1a and a suppression method selection unit 2a. Is provided.
- the same or corresponding parts as the components of the speech recognition apparatus 100 according to the first embodiment are denoted by the same reference numerals as those used in the first embodiment, and the description thereof is omitted or simplified. .
- the second prediction unit 1a is composed of a discriminator.
- NN is constructed and applied.
- a generally used acoustic feature such as MFCC or filter bank feature is used, and classification processing such as 2-class classification or multi-class classification is performed as a discriminator, and the suppression method with the highest recognition rate
- the NN that selects the identifiers of the two is constructed using the error back-propagation method.
- the second prediction unit 1a performs, for example, an acoustic feature value as an input, performs a 2-class or multi-class classification with a final output layer as a softmax layer, and uses a suppression method ID that leads to a speech recognition result with the highest speech recognition rate It is composed of NN as (identification). For NN teacher data, only the suppression method that leads to the speech recognition result with the highest speech recognition rate is set to “1”, the other methods are set to “0”, the recognition rate is multiplied by Sigmoid, etc.
- Weighted data (Sigmoid ((recognition rate of the system— (max (recognition rate) ⁇ min (recognition rate) / 2)) / ⁇ ) can be used, where ⁇ is a scaling factor.
- SVM support vector machine
- the suppression method selection unit 2a refers to the suppression method ID predicted by the second prediction unit 1a, and selects the noise suppression unit 3 that performs noise suppression from the plurality of noise suppression units 3a, 3b, and 3c.
- the noise suppression unit 3 can employ a spectrum removal method (SS), an adaptive filter method, a method using NN, and the like.
- the suppression method selection unit 2a outputs a control instruction to the selected noise suppression unit 3 so as to perform noise suppression processing.
- FIG. 5 is a flowchart showing the operation of the speech recognition apparatus 100a according to the second embodiment.
- the same steps as those of the speech recognition apparatus 100 according to Embodiment 1 are denoted by the same reference numerals as those used in FIG. 3, and the description thereof is omitted or simplified.
- noise voice data and an acoustic feature amount of the noise voice data are input to the voice recognition device 100a via an external microphone or the like.
- the second predicting unit 1a is the most speech by the NN in a short-time Fourier transform frame unit of the input acoustic feature amount.
- a suppression method ID of a noise suppression method that leads to a speech recognition result with a high recognition rate is predicted (step ST11).
- the second prediction unit 1a obtains the mode or average value of the suppression method ID predicted in units of frames in step ST11, and acquires the mode or average suppression method ID as the prediction suppression method ID (step S11). ST12).
- the suppression method selection unit 2a refers to the prediction suppression method ID acquired in step ST12, selects the noise suppression unit 3 corresponding to the acquired prediction suppression method ID, and performs noise suppression processing on the selected noise suppression unit 3.
- a control instruction is output so as to be performed (step ST13). Thereafter, the same processing as that in step ST6 and step ST7 described in the first embodiment is performed.
- a discriminator is applied, and an acoustic feature quantity is input, and an output is composed of NNs that are IDs of suppression methods that lead to a speech recognition result with the highest speech recognition rate.
- the second prediction unit 1a and the noise suppression unit 3 that leads the speech recognition result with the highest speech recognition rate from the plurality of noise suppression units 3 with reference to the suppression method ID predicted by the second prediction unit 1a are selected.
- a suppression method selection unit 2a that outputs a control instruction to the selected noise suppression unit 3, and a plurality of processing units corresponding to each of the plurality of noise suppression processes, and noise based on the control instruction of the suppression method selection unit 2a Since it is configured to include the noise suppression unit 3 that performs noise suppression of speech data and the speech recognition unit 4 that performs speech recognition of speech data that has undergone noise suppression processing, the processing amount of speech recognition is not increased. And noise suppression techniques Without performing the noise suppression process for-option, it is possible to select a valid noise suppression techniques.
- Embodiment 3 the acoustic feature amount is input to the first prediction unit 1 or the second prediction unit 1a for each short-time Fourier transform frame, and the speech recognition rate or suppression is input for each input frame.
- the structure which estimates method ID was shown.
- an utterance closest to the acoustic feature amount of the noise speech data actually input to the speech recognition device is selected from previously learned data using the acoustic feature amount of the utterance unit. 1 shows a configuration for selecting a noise suppression unit based on a speech recognition rate of a selected utterance.
- FIG. 6 is a block diagram illustrating a configuration of the speech recognition apparatus 100b according to the third embodiment.
- the speech recognition apparatus 100b according to the third embodiment replaces the first prediction unit 1 and the suppression technique selection unit 2 of the speech recognition apparatus 100 described in the first embodiment, and includes a feature amount calculation unit 5 and a similarity calculation unit 6.
- the third prediction unit 1c and the suppression method selection unit 2b including the recognition rate database 7 are provided.
- the same or corresponding parts as the components of the speech recognition apparatus 100 according to the first embodiment are denoted by the same reference numerals as those used in the first embodiment, and the description thereof is omitted or simplified. .
- the feature quantity calculation unit 5 constituting the third prediction unit 1c calculates an acoustic feature quantity in units of utterances from the input noise voice data. The details of the method for calculating the acoustic feature amount for each utterance will be described later.
- the similarity calculation unit 6 refers to the recognition rate database 7, collates the acoustic feature amount of the utterance unit calculated by the feature amount calculation unit 5 with the acoustic feature amount stored in the recognition rate database 7, and determines the acoustic feature amount. The similarity is calculated.
- the similarity calculation unit 6 acquires a set of speech recognition rates when noise suppression is performed by the noise suppression units 3a, 3b, and 3c associated with the acoustic feature amount having the highest similarity among the calculated similarities.
- the suppression method selection unit 2b refers to the set of speech recognition rates input from the similarity calculation unit 6, and selects the noise suppression unit 3 that performs noise suppression from the plurality of noise suppression units 3a, 3b, and 3c.
- the recognition rate database 7 is a storage area in which acoustic feature amounts of a plurality of learning data and speech recognition rates when the noise feature units 3a, 3b, and 3c suppress noise are associated and stored. .
- FIG. 7 is a diagram illustrating a configuration example of the recognition rate database 7 of the speech recognition apparatus 100b according to the third embodiment.
- the recognition rate database 7 includes the acoustic feature amount of the learning data, and the speech in which each learning data is subjected to noise suppression processing by each noise suppression unit (first, second, and third noise suppression units in the example of FIG. 7).
- the voice recognition rate of the data is stored in association with each other.
- FIG. 7 is a storage area in which acoustic feature amounts of a plurality of learning data and speech recognition rates when the noise feature units 3a, 3b, and 3c suppress noise are associated and stored.
- FIG. 7 is a diagram illustrating a configuration example of the recognition rate database 7 of the speech recognition apparatus 100b according to the third embodiment.
- the speech recognition rate of the speech data in which the first noise suppression unit has performed the noise suppression processing with respect to the learning data of the first acoustic feature amount V (r1) is 80%
- the second The voice recognition rate of the voice data subjected to the noise suppression processing by the noise suppression unit is 75%
- the voice recognition rate of the voice data subjected to the noise suppression processing by the third noise suppression unit is 78%.
- the recognition rate database 7 may be configured to cluster the learning data, store the recognition rate of the clustered learning data and the acoustic feature amount in association with each other, and store the data amount while suppressing the data amount.
- the acoustic feature quantity of the utterance unit an average vector of acoustic feature quantities, an average likelihood vector by Universal background model (UBM), an i-vector, or the like can be applied.
- the feature amount calculation unit 5 calculates the above-described acoustic feature amount in units of utterances for each of the recognition target noise sound data. For example, when an i-vector is applied as an acoustic feature, a Gaussian mixture model (GMM) is applied to the utterance r, and the obtained supervector V (r) is obtained in advance as a UBM supervector.
- GBM Gaussian mixture model
- the similarity between acoustic features of the utterance unit as shown in the following equation (2), measure using a Euclid distance or cosine similarity closest to the current evaluation data r e from in the training data r t to select a speech r't.
- the similarity is represented by sim
- an utterance represented by the following formula (3) is selected.
- FIG. 8 is a flowchart showing the operation of the speech recognition apparatus 100b according to the third embodiment.
- the same steps as those of the speech recognition apparatus 100 according to Embodiment 1 are denoted by the same reference numerals as those used in FIG. 3, and the description thereof is omitted or simplified.
- noise voice data is input to the voice recognition device 100b via an external microphone or the like.
- the feature amount calculation unit 5 calculates an acoustic feature amount from the input noise voice data (step ST22).
- the similarity calculation unit 6 compares the acoustic feature quantity calculated in step ST22 with the acoustic feature quantity of the learning data stored in the recognition rate database 7, and calculates the similarity degree (step ST23).
- the similarity calculation unit 6 selects the acoustic feature amount indicating the highest similarity among the similarity of the acoustic feature amount calculated in step ST23, and is associated with the selected acoustic feature amount with reference to the recognition rate database 7.
- a set of recognition rates is acquired (step ST24). In step ST24, when the Euclid distance is used as the similarity between the acoustic feature quantities, a pair of recognition rates with the shortest distance is acquired.
- the suppression technique selection unit 2b selects the noise suppression unit 3 that exhibits the highest recognition rate from the recognition rate group acquired in step ST24, and performs noise suppression processing on the selected noise suppression unit 3.
- a control instruction is output (step ST25). Thereafter, the same processing as in steps ST6 and ST7 described above is performed.
- the calculated acoustic feature amount and the acoustic feature of the learning data are referred to by referring to the feature amount calculation unit 5 that calculates the acoustic feature amount from the noise voice data and the recognition rate database 7.
- the similarity calculation unit 6 that calculates the similarity to the amount and acquires the speech recognition rate pair associated with the acoustic feature amount indicating the highest similarity, and the highest among the acquired speech recognition rate pairs Since it comprises the suppression method selection part 2b which selects the noise suppression part 3 which shows a speech recognition rate, the speech recognition performance can be predicted in speech units, and the speech recognition performance is highly predicted and fixed. By using the dimension feature amount, there is an effect that the similarity can be easily calculated.
- the similarity calculation unit 6 refers to an external database and calculates the similarity with the acoustic feature amount. You may comprise so that acquisition of a recognition rate may be performed.
- a delay occurs when speech recognition is performed in units of utterances, but if the delay cannot be allowed, the acoustic feature value is calculated using the utterances for the first few seconds after the start of the utterance. You may comprise so that it may refer. Further, in the case where the environment with the utterance made before the utterance subject to the speech recognition does not change, the speech recognition is performed using the selection result of the noise suppression unit 3 in the previous utterance. Also good.
- FIG. 9 is a block diagram showing the configuration of the speech enhancement apparatus 200 according to the fourth embodiment.
- the speech enhancement apparatus 200 according to the fourth embodiment includes the third prediction unit 1c and the suppression provided with the feature amount calculation unit 5, the similarity calculation unit 6, and the recognition rate database 7 of the speech recognition apparatus 100b illustrated in the third embodiment.
- a fourth prediction unit 1d and a suppression method selection unit 2c each including a feature amount calculation unit 5, a similarity calculation unit 6a, and an acoustic index database 8 are provided. Further, the voice recognition unit 4 is not provided.
- the same or corresponding parts as those of the speech recognition apparatus 100b according to the third embodiment are denoted by the same reference numerals as those used in the third embodiment, and the description thereof is omitted or simplified. .
- the acoustic index database 8 is a storage area in which acoustic feature quantities of a plurality of learning data are stored in association with acoustic indices when the respective learning data are noise-suppressed by the noise suppression units 3a, 3b, and 3c.
- the acoustic index is PESQ or SNR / SDR calculated from the emphasized speech in which the noise is suppressed and the noise speech before the noise is suppressed.
- the acoustic index database 8 may be configured to cluster the learning data, store the acoustic index of the clustered learning data and the acoustic feature amount in association with each other, and store the data while suppressing the data amount.
- the similarity calculation unit 6 a refers to the acoustic index database 8, collates the acoustic feature amount of the utterance unit calculated by the feature amount calculation unit 5 with the acoustic feature amount stored in the acoustic index database 8, and determines the acoustic feature amount The similarity is calculated.
- the similarity calculation unit 6a acquires a set of acoustic indices associated with the acoustic feature quantity having the highest similarity among the calculated similarities, and outputs it to the suppression method selection unit 2c.
- the suppression method selection unit 2c refers to the set of acoustic indices input from the similarity calculation unit 6a, and selects the noise suppression unit 3 that performs noise suppression from the plurality of noise suppression units 3a, 3b, and 3c.
- FIG. 10 is a flowchart showing the operation of the speech enhancement apparatus 200 according to the fourth embodiment. It is assumed that noise speech data is input to the speech enhancement device 200 via an external microphone or the like.
- the feature quantity calculation unit 5 calculates an acoustic feature quantity from the input noise voice data (step ST32).
- the similarity calculation unit 6a compares the acoustic feature quantity calculated in step ST32 with the acoustic feature quantity of the learning data stored in the acoustic index database 8, and calculates the similarity degree (step ST33).
- the similarity calculation unit 6a selects an acoustic feature amount that indicates the highest similarity among the similarities of the acoustic feature amounts calculated in step ST33, and acquires a set of acoustic indicators associated with the selected acoustic feature amount. (Step ST34).
- the suppression technique selection unit 2c selects the noise suppression unit 3 that indicates the highest acoustic index among the set of acoustic indexes acquired in step ST34, and performs noise suppression processing on the selected noise suppression unit 3.
- a control instruction is output (step ST35).
- the noise suppression unit 3 to which the control instruction is input in step ST35 performs processing for suppressing the noise signal on the actual noise speech data input in step ST31 to acquire and output the emphasized speech (step ST36). . Thereafter, the flowchart returns to the process of step ST31 and repeats the above-described process.
- the calculated acoustic feature quantity and the acoustic feature of the learning data are referred to by referring to the feature quantity calculation unit 5 that calculates the acoustic feature quantity from the noise voice data and the acoustic index database 8.
- the suppression method selection unit 2c that selects the noise suppression unit 3 indicating the voice recognition performance can be predicted in units of utterances, the speech recognition performance is highly predicted, and fixed-dimension features
- the similarity calculation unit 6a refers to an external database and calculates the similarity with the acoustic feature amount. You may comprise so that an acoustic parameter
- a delay occurs when speech recognition is performed in units of utterances, but if the delay cannot be tolerated, the acoustic feature value is calculated using the utterances for the first few seconds after the start of the utterance. You may comprise so that it may refer. Further, when the environment with the utterance performed before the utterance that is the target of the enhanced speech acquisition does not change, the enhanced speech is acquired using the selection result of the noise suppression unit 3 in the previous utterance. It may be configured.
- FIG. 5 The speech recognition devices 100, 100a, and 100b of Embodiment 1-3 and the speech enhancement device 200 of Embodiment 4 described above are applied to, for example, a navigation system, a telephone-compatible system, and an elevator that have a voice call function. Can do.
- a navigation system a telephone-compatible system
- an elevator that have a voice call function. Can do.
- FIG. 11 is a functional block diagram showing a configuration of the navigation system 300 according to the fifth embodiment.
- the navigation system 300 is a device that is mounted on a vehicle and performs route guidance to a destination, for example, and includes an information acquisition device 301, a control device 302, an output device 303, an input device 304, a voice recognition device 100, a map database 305, A route calculation device 306 and a route guidance device 307 are provided.
- the operation of each device of the navigation system 300 is centrally controlled by the control device 302.
- the information acquisition device 301 includes, for example, current position detection means, wireless communication means, surrounding information detection means, and the like, and acquires information detected by the current position of the host vehicle, the surroundings of the host vehicle, and other vehicles.
- the output device 303 includes, for example, a display unit, a display control unit, a voice output unit, a voice control unit, and the like, and notifies the user of information.
- the input device 304 is realized by voice input means such as a microphone and operation input means such as buttons and a touch panel, and receives information input from the user.
- the speech recognition device 100 is a speech recognition device having the configuration and functions described in the first embodiment, performs speech recognition on noise speech data input via the input device 304, and acquires a speech recognition result. And output to the control device 302.
- the map database 305 is a storage area for storing map data, and is realized as a storage device such as an HDD (Hard Disk Drive) or a RAM (Random Access Memory).
- the route calculation device 306 uses the current position of the vehicle acquired by the information acquisition device 301 as a departure point, uses the voice recognition result of the voice recognition device 100 as a destination, and based on the map data stored in the map database 305. The route from to the destination is calculated.
- the route guidance device 307 guides the host vehicle according to the route calculated by the route calculation device 306.
- the speech recognition device 100 When the noise speech data including the user's utterance is input from the microphone constituting the input device 304, the speech recognition device 100 performs the processing shown in the flowchart of FIG. 3 on the noise speech data. To obtain a speech recognition result.
- the route calculation device 306 Based on the information input from the control device 302 and the information acquisition device 301, the route calculation device 306 uses the current position of the vehicle acquired by the information acquisition device 301 as the departure point and the information indicated by the voice recognition result as the destination. The route from the starting point to the destination is calculated based on the map data.
- the route guidance device 307 outputs the route guidance information calculated according to the route calculated by the route calculation unit 306 via the output device 303, and performs route guidance for the user.
- the speech recognition apparatus 100 generates a speech recognition result indicating a favorable speech recognition rate for noise speech data including a user's utterance input to the input device 304.
- the noise suppression unit 3 that is predicted to perform the noise suppression process performs speech recognition, so that the route calculation can be performed based on the speech recognition result with a good speech recognition rate. Route guidance can be performed.
- the configuration in which the voice recognition device 100 described in the first embodiment is applied to the navigation system 300 has been described.
- the voice recognition device 100a described in the second embodiment and the third embodiment are described.
- the speech recognition device 100b shown in FIG. 5 or the speech enhancement device 200 shown in Embodiment 4 may be applied.
- the speech enhancement apparatus 200 is applied to the navigation system 300, the navigation system 300 side has a function of recognizing the enhanced speech.
- the present invention can be freely combined with each embodiment, modified any component of each embodiment, or omitted any component in each embodiment. Is possible.
- the speech recognition apparatus and the speech enhancement apparatus can select a noise suppression method that provides a favorable speech recognition rate or acoustic index, a device having a call function such as a navigation system, a telephone-compatible system, and an elevator Can be applied to.
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Abstract
Description
実施の形態1.
まず、図1は、実施の形態1に係る音声認識装置100の構成を示すブロック図である。
音声認識装置100は、第1の予測部1、抑圧手法選択部2、騒音抑圧部3および音声認識部4を備えて構成されている。
第1の予測部1は、回帰器で構成される。回帰器としては、例えばNeural network(以下、NNと称する)を構築して適用する。NNの構築では、Mel-frequency Cepstral Coefficient(MFCC)またはフィルタバンク特徴利用など、一般的に利用される音響特徴量を利用し、回帰器として0以上1以下となる音声認識率を直接算出するNNを、例えば誤差逆伝搬法などを用いて構築する。誤差逆伝搬法とは、ある学習データが与えられた時に、当該学習データとNNの出力の誤差が小さくなるように各層の間の結合荷重・バイアス等を修正する学習法である。第1の予測部1は、例えば入力を音響特徴量とし、出力を音声認識率とするNNにより、入力された音響特徴量の音声認識率を予測する。 Hereinafter, in order to explain the present invention in more detail, modes for carrying out the present invention will be described with reference to the accompanying drawings.
Embodiment 1 FIG.
First, FIG. 1 is a block diagram showing the configuration of the
The
The first prediction unit 1 is composed of a regressor. For example, a neural network (hereinafter referred to as NN) is constructed and applied as the regressor. In the construction of the NN, an acoustic feature that is generally used, such as Mel-frequency Cepstral Coefficient (MFCC) or filter bank feature, is used to directly calculate a speech recognition rate of 0 to 1 as a regressor. Is constructed using, for example, the error back-propagation method. The error back-propagation method is a learning method in which, when given learning data is given, the coupling load / bias between the layers is corrected so that the error between the learning data and the output of the NN becomes small. The first predicting unit 1 predicts the speech recognition rate of the input acoustic feature amount by, for example, the NN having the input as the acoustic feature amount and the output as the speech recognition rate.
図2Aは、実施の形態1に係る音声認識装置100のハードウェア構成を示し、処理回路がハードウェアで実行される場合のブロック図を示す。図2Aに示すように、処理回路101が専用のハードウェアである場合、第1の予測部1、抑圧手法選択部2、騒音抑圧部3および音声認識部4の各機能それぞれを処理回路で実現してもよいし、各部の機能をまとめて処理回路で実現してもよい。 The first prediction unit 1, the suppression method selection unit 2, the
FIG. 2A shows a hardware configuration of the
図2Bに示すように、処理回路がプロセッサ102である場合、第1の予測部1、抑圧手法選択部2、騒音抑圧部3および音声認識部4の各機能は、ソフトウェア、ファームウェア、またはソフトウェアとファームウェアとの組み合わせにより実現される。ソフトウェアやファームウェアはプログラムとして記述され、メモリ103に格納される。プロセッサ102はメモリ103に記憶されたプログラムを読み出して実行することにより、各部の機能を実行する。ここで、メモリ103とは、例えばRAM、ROM、フラッシュメモリーなどの不揮発性または揮発性の半導体メモリや、磁気ディスク、光ディスクなどが該当する。 FIG. 2B shows a hardware configuration of the
As shown in FIG. 2B, when the processing circuit is the
まず、回帰器を適用した第1の予測部1は、音響特徴量を入力とし、出力を音声認識率とするNNで構成されている。第1の予測部1は、音響特徴量が短時間フーリエ変換のフレーム毎に入力されると、NNにより各騒音抑圧部3a,3b,3cのそれぞれが音声認識率を予測する。即ち、第1の予測部1は、それぞれ異なる騒音抑圧処理を適用した場合の音声認識率を音響特徴量のフレーム毎に算出する。抑圧手法選択部2は、第1の予測部1が算出した各騒音抑圧部3a,3b,3cを適用した場合の音声認識率を参照し、最も音声認識率が高い音声認識結果を導く騒音抑圧部3を選択し、選択した騒音抑圧部3に対して制御指示を出力する。 Next, detailed configurations of the first prediction unit 1 and the suppression method selection unit 2 will be described.
First, the 1st prediction part 1 to which the regressor is applied is comprised by NN which uses an acoustic feature-value as an input and uses an output as a speech recognition rate. When the acoustic feature quantity is input for each frame of the short-time Fourier transform, the first prediction unit 1 predicts the speech recognition rate by each of the
音声認識装置100には、外部のマイクなどを介して騒音音声データと、当該騒音音声データの音響特徴量とが入力されるものとする。なお、騒音音声データの音響特徴量は、外部の特徴量算出手段により算出されるものとする。
騒音音声データ、および当該騒音音声データの音響特徴量が入力されると(ステップST1)、第1の予測部1は入力された音響特徴量の短時間フーリエ変換のフレーム単位で、NNにより各騒音抑圧部3a,3b,3cで騒音抑圧処理を行った場合の音声認識率を予測する(ステップST2)。なお、ステップST2の処理は、設定された複数のフレームに対して処理が繰り返し行われる。第1の予測部1は、ステップST2においてフレーム単位且つ複数のフレームについて予測した音声認識率の平均、最大値、または最小値を求め、各騒音抑圧部3a,3b,3cで処理を行った場合のそれぞれの予測認識率を算出する(ステップST3)。第1の予測部1は算出した予測認識率を各騒音抑圧部3a,3b,3cと紐付けて抑圧手法選択部2に出力する(ステップST4)。 FIG. 3 is a flowchart showing the operation of the
It is assumed that the
When the noise voice data and the acoustic feature quantity of the noise voice data are input (step ST1), the first predicting unit 1 performs each noise by NN in a frame unit of short-time Fourier transform of the input acoustic feature quantity. A speech recognition rate is predicted when noise suppression processing is performed by the
例えば従来の技術では、3つ候補となる騒音抑圧手法があった場合には、3つの手法すべてで騒音抑圧処理を行いその結果に基づいて最もよい騒音抑圧処理を選んでいたが、この実施の形態1によれば、候補となる騒音抑圧手法が3つあった場合にも、あらかじめ最も性能がよいであろう手法が予測できるので、その選ばれた手法でのみ騒音抑圧処理を行うことで騒音抑圧処理にかかる計算量を削減することができるという利点が得られる。 As described above, according to the first embodiment, the first predicting unit 1 configured by the regressor, configured by the NN having the acoustic feature quantity as input and the output as the speech recognition rate, With reference to the speech recognition rate predicted by the prediction unit 1, the
For example, in the conventional technology, when there are three candidate noise suppression methods, the noise suppression processing is performed by all three methods and the best noise suppression processing is selected based on the result. According to the first aspect, even when there are three candidate noise suppression methods, a method that will have the best performance can be predicted in advance. Therefore, noise suppression processing is performed by performing noise suppression processing only with the selected method. There is an advantage that the amount of calculation for processing can be reduced.
上述した実施の形態1では、回帰器を用いて音声認識率の高い音声認識結果を導く騒音抑圧部3を選択する構成を示したが、この実施の形態2では識別器を用いて音声認識率の高い音声認識結果を導く騒音抑圧部3を選択する構成を示す。
図4は、実施の形態2に係る音声認識装置100aの構成を示すブロック図である。
実施の形態2の音声認識装置100aは、実施の形態1で示した音声認識装置100の第1の予測部1および抑圧手法選択部2に替えて第2の予測部1aおよび抑圧手法選択部2aを設けて構成している。なお、以下では、実施の形態1に係る音声認識装置100の構成要素と同一または相当する部分には、実施の形態1で使用した符号と同一の符号を付して説明を省略または簡略化する。 Embodiment 2. FIG.
In the first embodiment described above, the configuration has been shown in which the
FIG. 4 is a block diagram illustrating a configuration of the speech recognition apparatus 100a according to the second embodiment.
The speech recognition apparatus 100a according to Embodiment 2 replaces the first prediction unit 1 and the suppression method selection unit 2 of the
もちろんSVM(support vector machine)などの他の分類器を使うことも考えられる。 The
Of course, it is possible to use other classifiers such as SVM (support vector machine).
図5は、実施の形態2に係る音声認識装置100aの動作を示すフローチャートである。なお、以下では実施の形態1に係る音声認識装置100と同一のステップには図3で使用した符号と同一の符号を付し、説明を省略または簡略化する。
音声認識装置100aには、外部のマイクなどを介して騒音音声データと、当該騒音音声データの音響特徴量とが入力されるものとする。
騒音音声データ、および当該騒音音声データの音響特徴量が入力されると(ステップST1)、第2の予測部1aは入力された音響特徴量の短時間フーリエ変換のフレーム単位で、NNにより最も音声認識率の高い音声認識結果を導く騒音抑圧手法の抑圧手法IDを予測する(ステップST11)。 Next, the operation of the speech recognition apparatus 100a will be described.
FIG. 5 is a flowchart showing the operation of the speech recognition apparatus 100a according to the second embodiment. In the following, the same steps as those of the
It is assumed that noise voice data and an acoustic feature amount of the noise voice data are input to the voice recognition device 100a via an external microphone or the like.
When the noise voice data and the acoustic feature amount of the noise voice data are input (step ST1), the
上述した実施の形態1,2では、音響特徴量を短時間フーリエ変換のフレーム毎に第1の予測部1または第2の予測部1aに入力し、入力されたフレーム毎に音声認識率または抑圧手法IDを予測する構成を示した。一方、この実施の形態3では、発話単位の音響特徴量を用いて、予め学習したデータの中から、実際に音声認識装置に入力される騒音音声データの音響特徴量に最も近い発話を選択し、選択した発話の音声認識率に基づいて騒音抑圧部の選択を行う構成を示す。
In the first and second embodiments described above, the acoustic feature amount is input to the first prediction unit 1 or the
実施の形態3の音声認識装置100bは、実施の形態1で示した音声認識装置100の第1の予測部1および抑圧手法選択部2に替えて、特徴量算出部5、類似度算出部6、認識率データベース7を備える第3の予測部1cおよび抑圧手法選択部2bを設けて構成している。
なお、以下では、実施の形態1に係る音声認識装置100の構成要素と同一または相当する部分には、実施の形態1で使用した符号と同一の符号を付して説明を省略または簡略化する。 FIG. 6 is a block diagram illustrating a configuration of the
The
In the following description, the same or corresponding parts as the components of the
図7は、実施の形態3に係る音声認識装置100bの認識率データベース7の構成例を示す図である。
認識率データベース7は、学習データの音響特徴量と、各学習データを各騒音抑圧部(図7の例では、第1,第2,第3の騒音抑圧部)により騒音抑圧処理を行った音声データの音声認識率とを対応付けて格納している。図7において、例えば、第1の音響特徴量V(r1)の学習データに対して、第1の騒音抑圧部が騒音抑圧処理を行った音声データの音声認識率が80%であり、第2の騒音抑圧部が騒音抑圧処理を行った音声データの音声認識率が75%であり、第3の騒音抑圧部が騒音抑圧処理を行った音声データの音声認識率が78%であることを示している。なお、認識率データベース7は、学習データをクラスタリングし、クラスタリングした学習データの認識率と、音響特徴量とを対応付けて記憶し、データ量を抑制して格納するように構成してもよい。 The
FIG. 7 is a diagram illustrating a configuration example of the
The
発話単位の音響特徴量としては、音響特徴量の平均ベクトル、Universal background model(UBM)による平均尤度ベクトル、i-vectorなどが適用可能である。特徴量算出部5は、上述した音響特徴量を、認識対象の騒音音声データそれぞれに対して、発話単位で算出する。例えば音響特徴量としてi-vectorを適用する場合には、Gaussian mixture model (GMM)を発話rに対して適応し、得られたスーパーベクトルV(r)を、あらかじめ求めておいたUBMのスーパーベクトルvと低ランクの全変数平面を張る基底ベクトルから成る行列Tにより、以下の式(1)に基づいて因子分解する。
V(r)=v+Tw(r) (1)
上述した式(1)により得られるw(r)がi-vectorである。 Next, the details of the calculation of the acoustic feature amount for each utterance by the feature
As the acoustic feature quantity of the utterance unit, an average vector of acoustic feature quantities, an average likelihood vector by Universal background model (UBM), an i-vector, or the like can be applied. The feature
V (r) = v + Tw (r) (1)
W (r) obtained by the above equation (1 ) is an i-vector.
The similarity between acoustic features of the utterance unit, as shown in the following equation (2), measure using a Euclid distance or cosine similarity closest to the current evaluation data r e from in the training data r t to select a speech r't. When the similarity is represented by sim, an utterance represented by the following formula (3) is selected.
なお、上述した説明では、騒音抑圧手法が2つの場合を例に説明を行ったが、騒音抑圧手法が3つ以上の場合にも適用可能である。 If the word error rate W tr (i, r t ) obtained using the i-th
In the above description, the case where there are two noise suppression methods has been described as an example, but the present invention can also be applied to a case where there are three or more noise suppression methods.
図8は、実施の形態3に係る音声認識装置100bの動作を示すフローチャートである。なお、以下では実施の形態1に係る音声認識装置100と同一のステップには図3で使用した符号と同一の符号を付し、説明を省略または簡略化する。
音声認識装置100bには、外部のマイクなどを介して騒音音声データが入力されるものとする。
騒音音声データが入力されると(ステップST21)、特徴量算出部5は入力された騒音音声データから音響特徴量を算出する(ステップST22)。類似度算出部6は、ステップST22で算出された音響特徴量と、認識率データベース7に格納された学習データの音響特徴量とを比較し、類似度を算出する(ステップST23)。類似度算出部6は、ステップST23で算出した音響特徴量の類似度のうち最も高い類似度を示す音響特徴量を選択し、認識率データベース7を参照して選択した音響特徴量に対応付けられた認識率の組を取得する(ステップST24)。ステップST24において、音響特徴量間の類似性としてEuclid距離を用いた場合には、最も距離の短い認識率の組を取得する。 Next, the operation of the
FIG. 8 is a flowchart showing the operation of the
It is assumed that noise voice data is input to the
When noise voice data is input (step ST21), the feature
上述した実施の形態3では、学習データの音響特徴量と音声認識率とを対応付けた認識率データベース7を参照して騒音抑圧手法を選択する構成を示したが、この実施の形態4では学習データの音響特徴量と音響指標とを対応付けた音響指標データベースを参照して騒音抑圧手法を選択する構成を示す。
図9は、実施の形態4に係る音声強調装置200の構成を示すブロック図である。
実施の形態4の音声強調装置200は、実施の形態3で示した音声認識装置100bの特徴量算出部5、類似度算出部6および認識率データベース7を備えた第3の予測部1cおよび抑圧手法選択部2bに替えて、特徴量算出部5、類似度算出部6aおよび音響指標データベース8を備えた第4の予測部1dおよび抑圧手法選択部2cを設けて構成している。また、音声認識部4を備えていない。
なお、以下では、実施の形態3に係る音声認識装置100bの構成要素と同一または相当する部分には、実施の形態3で使用した符号と同一の符号を付して説明を省略または簡略化する。
In the third embodiment described above, the configuration in which the noise suppression method is selected with reference to the
FIG. 9 is a block diagram showing the configuration of the
The
In the following, the same or corresponding parts as those of the
抑圧手法選択部2cは、類似度算出部6aから入力された音響指標の組を参照し、複数の騒音抑圧部3a,3b,3cから騒音抑圧を行う騒音抑圧部3を選択する。 The
The suppression
図10は、実施の形態4に係る音声強調装置200の動作を示すフローチャートである。音声強調装置200には、外部のマイクなどを介して騒音音声データが入力されるものとする。
騒音音声データが入力されると(ステップST31)、特徴量算出部5は入力された騒音音声データから音響特徴量を算出する(ステップST32)。類似度算出部6aは、ステップST32で算出された音響特徴量と、音響指標データベース8に格納された学習データの音響特徴量とを比較し、類似度を算出する(ステップST33)。類似度算出部6aは、ステップST33で算出した音響特徴量の類似度のうち最も高い類似度を示す音響特徴量を選択し、選択した音響特徴量に対応付けられた音響指標の組を取得する(ステップST34)。 Next, the operation of the
FIG. 10 is a flowchart showing the operation of the
When noise voice data is input (step ST31), the feature
上述した実施の形態1-3の音声認識装置100,100a,100bおよび実施の形態4の音声強調装置200は、例えば音声による通話機能を備えたナビゲーションシステム、電話対応システム、エレベータなどに適用することができる。この実施の形態5では、実施の形態1の音声認識装置をナビゲーションシステムに適用した場合について示す。
図11は、実施の形態5に係るナビゲーションシステム300の構成を示す機能ブロック図である。
ナビゲーションシステム300は、例えば車両に搭載されて目的地までの経路案内を実行する装置であり、情報取得装置301、制御装置302、出力装置303、入力装置304、音声認識装置100、地図データベース305、経路算出装置306および経路案内装置307を備える。ナビゲーションシステム300の各装置の動作は、制御装置302によって統括的に制御される。
The
FIG. 11 is a functional block diagram showing a configuration of the
The
Claims (9)
- 入力された騒音音声データに対して、それぞれ異なる手法の騒音抑圧処理を行う複数の騒音抑圧部と、
前記騒音抑圧部により騒音信号が抑圧された音声データの音声認識を行う音声認識部と、
前記入力された騒音音声データの音響特徴量から、前記騒音音声データを前記複数の騒音抑圧部によりそれぞれ騒音抑圧処理を行った場合に得られる音声認識率を予測する予測部と、
前記予測部が予測した音声認識率に基づいて、前記複数の騒音抑圧部から前記騒音音声データに対して騒音抑圧処理を行う騒音抑圧部を選択する抑圧手法選択部とを備えた音声認識装置。 A plurality of noise suppression units that perform noise suppression processing of different methods on the input noise voice data,
A voice recognition unit for performing voice recognition of voice data in which a noise signal is suppressed by the noise suppression unit;
A prediction unit that predicts a speech recognition rate obtained when noise suppression processing is performed on each of the noise speech data by the plurality of noise suppression units, based on an acoustic feature amount of the input noise speech data;
A speech recognition apparatus comprising: a suppression method selection unit that selects a noise suppression unit that performs noise suppression processing on the noise speech data from the plurality of noise suppression units based on the speech recognition rate predicted by the prediction unit. - 前記予測部は、前記音響特徴量の短時間フーリエ変換のフレーム毎に、前記音声認識率の予測を行うこと特徴とする請求項1記載の音声認識装置。 The speech recognition apparatus according to claim 1, wherein the prediction unit predicts the speech recognition rate for each frame of a short-time Fourier transform of the acoustic feature amount.
- 前記予測部は、前記音響特徴量を入力とし、前記音響特徴量の音声認識率を出力とするニューラルネットワークで構成されることを特徴とする請求項1記載の音声認識装置。 The speech recognition apparatus according to claim 1, wherein the prediction unit is configured by a neural network that receives the acoustic feature amount as an input and outputs a speech recognition rate of the acoustic feature amount.
- 前記予測部は、前記音響特徴量を入力として分類処理を行い、音声認識率の高い前記騒音抑圧部を示す情報を出力とするニューラルネットワークで構成されることを特徴とする請求項1記載の音声認識装置。 The speech according to claim 1, wherein the prediction unit includes a neural network that performs classification processing using the acoustic feature amount as an input and outputs information indicating the noise suppression unit having a high speech recognition rate. Recognition device.
- 前記予測部は、前記騒音音声データから発話単位で音響特徴量を算出する特徴量算出部と、前記特徴量算出部が算出した音響特徴量とあらかじめ蓄積された音響特徴量との類似度に基づいて、予め蓄積された音声認識率を取得する類似度算出部とを備えたことを特徴とする請求1記載の音声認識装置。 The prediction unit is based on a feature amount calculation unit that calculates an acoustic feature amount in speech units from the noise voice data, and a similarity between the acoustic feature amount calculated by the feature amount calculation unit and the acoustic feature amount accumulated in advance. The speech recognition apparatus according to claim 1, further comprising a similarity calculation unit that acquires a speech recognition rate accumulated in advance.
- 入力された騒音音声データに対して、それぞれ異なる手法の騒音抑圧処理を行う複数の騒音抑圧部と、
前記入力された騒音音声データから発話単位で音響特徴量を算出する特徴量算出部と、前記特徴量算出部が算出した音響特徴量とあらかじめ蓄積された音響特徴量との類似度に基づいて、あらかじめ蓄積された音響指標を取得する類似度算出部とを有する予測部と、
前記類似度算出部が取得した音響指標に基づいて、前記複数の騒音抑圧部から前記騒音音声データの騒音抑圧処理を行う騒音抑圧部を選択する抑圧手法選択部とを備えた音声強調装置。 A plurality of noise suppression units that perform noise suppression processing of different methods on the input noise voice data,
Based on the similarity between the acoustic feature amount calculated by the feature amount calculation unit and the acoustic feature amount stored in advance by the feature amount calculation unit that calculates the acoustic feature amount in units of utterances from the input noise voice data, A prediction unit having a similarity calculation unit for acquiring a pre-stored acoustic index;
A speech enhancement apparatus comprising: a suppression method selection unit that selects a noise suppression unit that performs noise suppression processing of the noise speech data from the plurality of noise suppression units based on an acoustic index acquired by the similarity calculation unit. - 予測部が、入力された騒音音声データの音響特徴量から、前記騒音音声データを前記複数の騒音抑圧手法によりそれぞれ騒音抑圧処理を行った場合に得られる音声認識率を予測するステップと、
抑圧手法選択部が、前記予測された音声認識率に基づいて、前記騒音音声データに対して騒音抑圧処理を行う騒音抑圧部を選択するステップと、
前記選択された騒音抑圧部が、前記入力された騒音音声データの騒音抑圧処理を行うステップと、
音声認識部が、前記騒音抑圧処理により騒音信号が抑圧された音声データの音声認識を行うステップとを備えた音声認識方法。 A predicting unit predicting a speech recognition rate obtained when noise suppression processing is performed on each of the noise speech data by the plurality of noise suppression methods, based on an acoustic feature amount of the input noise speech data;
A suppression method selection unit selecting a noise suppression unit that performs noise suppression processing on the noise speech data based on the predicted speech recognition rate;
The selected noise suppression unit performing noise suppression processing of the input noise voice data;
A speech recognition method comprising: a speech recognition unit performing speech recognition of speech data in which a noise signal is suppressed by the noise suppression processing. - 予測部の特徴量算出部が、入力された騒音音声データから発話単位で音響特徴量を算出するステップと、
予測部の類似度算出部が、前記算出された音響特徴量とあらかじめ蓄積された音響特徴量との類似度に基づいて、あらかじめ蓄積された音響指標を取得するステップと、
抑圧手法選択部が、前記取得された音響指標に基づいて、前記騒音音声データに対して騒音抑圧処理を行う騒音抑圧部を選択するステップと、
前記選択された騒音抑圧部が、前記入力された騒音音声データの騒音抑圧処理を行うステップとを備えた音声強調装置。 A feature amount calculation unit of the prediction unit that calculates an acoustic feature amount in units of utterances from the input noise voice data;
A step of obtaining a pre-stored acoustic index based on the similarity between the calculated acoustic feature quantity and the pre-stored acoustic feature quantity, the similarity calculating section of the prediction unit;
A step of selecting a noise suppression unit that performs a noise suppression process on the noise speech data based on the acquired acoustic index;
The speech enhancement apparatus, wherein the selected noise suppression unit performs a noise suppression process on the input noise speech data. - 請求項1記載の音声認識装置と、
移動体の現在位置を当該移動体の出発地とし、前記音声認識装置の出力である音声認識結果を前記移動体の目的地とし、地図データを参照して、前記出発地から前記目的地までの経路を算出する経路算出装置と、
前記経路算出部が算出した経路に従って前記移動体の移動を案内する経路案内装置とを備えたナビゲーション装置。 A speech recognition device according to claim 1;
The current position of the moving object is the starting point of the moving object, the voice recognition result that is the output of the voice recognition device is the destination of the moving object, and the map data is referred to from the starting point to the destination. A route calculation device for calculating a route;
A navigation device comprising: a route guidance device that guides the movement of the mobile body according to the route calculated by the route calculation unit.
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